Under the influence: The celebrity factor in policy capture
Bibliographic record
Abstract
Abstract Celebrity is a form of policy influence that can occur under distinctive circumstances. This paper draws on the regulatory/policy capture literature to develop a model of celebrity capture that explains how interest groups can affect policy in the absence of economic clout or constituency mobilization. We posit that the likelihood of celebrity capture increases when several factors align: (1) a context open to change; (2) reduced oversight in decisionmaking processes; (3) organizations that have credibility and a halo effect due to their celebrity status; and (4) an uncoordinated sector with weak intermediary organizations. The analysis applies process tracing to account for the success of one celebrity‐founded and celebrity‐led organization, WE Charity, in shaping the design and being awarded sole‐source implementation of the CAD $543 million Canada Student Service Grant (CSSG) program during COVID‐19. The CSSG, which proposed to pay up to 100,000 students to “volunteer” in nonprofits over the course of a summer, quickly failed and became a public ethical scandal.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".